bert_uncased_L-2_H-128_A-2-mlm-multi-emails-hq (BERT-tiny)
This model is a fine-tuned version of google/bert_uncased_L-2_H-128_A-2 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 3.0981
- Accuracy: 0.4728
Model description
BERT-tiny fine-tuned on email data for eight epochs.
Intended uses & limitations
- this is mostly a test
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 8.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 3.8974 | 0.99 | 141 | 3.5129 | 0.4218 |
| 3.7009 | 1.99 | 282 | 3.3295 | 0.4452 |
| 3.5845 | 2.99 | 423 | 3.2219 | 0.4589 |
| 3.4976 | 3.99 | 564 | 3.1618 | 0.4666 |
| 3.4356 | 4.99 | 705 | 3.1002 | 0.4739 |
| 3.4493 | 5.99 | 846 | 3.1028 | 0.4746 |
| 3.4199 | 6.99 | 987 | 3.0857 | 0.4766 |
| 3.4086 | 7.99 | 1128 | 3.0981 | 0.4728 |
Framework versions
- Transformers 4.27.0.dev0
- Pytorch 2.0.0.dev20230129+cu118
- Datasets 2.8.0
- Tokenizers 0.13.1
- Downloads last month
- 24
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.
Model tree for postbot/bert_uncased_tiny-multi-emails-hq
Base model
google/bert_uncased_L-2_H-128_A-2